Artificial neural networks may be designed, constructed, and trained to perform a wide range of decision-making processes and predictive analyses. Such neural networks may be implemented as data structures including a plurality of nodes (or neurons) along with a defined set of interconnections between pairs of nodes, and a weight value associated with each interconnection. Such neural networks may be structured in layers, for example, a first layer of input nodes, one or more layers of internal nodes, and a layer of output nodes. After a neural network data structure has been generated and trained with an appropriate training data set, it may be used to perform decision-making processes and predictive analyses for various systems. For instance, a trained neural network may be deployed within a content distribution network and used perform tasks such as detecting patterns, predicting user behavior, data processing, function approximation, and the like.
Unfortunately, the performance of certain neural networks may tend to decrease over time. Such performance degradation may result in less accuracy of the predictions and other outputs generated by the neural network. After a neural network data structure has been generated, trained, and deployed, there may be little or no flexibility in altering the operation of the deployed neural network. In some cases, it may be possible to replace a neural network, but the time and resources required to generate and train replacement neural networks may be significant.